{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a28f5e6a",
   "metadata": {},
   "source": [
    "## Pandas 绘图 - 直方图（统计）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a8a25fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0703022",
   "metadata": {},
   "source": [
    "### 直方图\n",
    "random生成随机数百分比直方图，调用hist方法\n",
    "- 柱高表示数据的频数，柱宽表示各组数据的组距\n",
    "- 参数bins可以设置直方图方柱的个数上限，越大柱宽越小，数据分组越细致\n",
    "- 设置density参数为True，可以把频数转换为概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73291741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = pd.Series([1, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 6, 6])\n",
    "\n",
    "# 直方图\n",
    "# bins=5 表示分成5组\n",
    "# density=True就将频数改为频率，注意y轴值变化\n",
    "s.plot(kind='hist', bins=5, density=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d61bf414",
   "metadata": {},
   "source": [
    "kde图：核密度估计，用于弥补直方图有由于参数bins设置不合理导致的精度缺失问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0f0ae097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# kde图: 核密度估计\n",
    "s.plot(kind='hist', bins=5, density=True)\n",
    "s.plot(kind='kde')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
